{
 "cells": [
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   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "# Binary Knapsack\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d56bb6d-9f3b-45db-8e98-b50f27af7505",
   "metadata": {},
   "source": [
    "## Background\n",
    "\n",
    "Given a set of items, determine how many items to put in the knapsack to maximize their summed value.\n",
    "\n",
    "#### Define:\n",
    "\n",
    "- $x_i$ is the number of items from each type.\n",
    "\n",
    "- $v_i$ is the value of each item.\n",
    "\n",
    "- $w_i$ is the weight of each item.\n",
    "\n",
    "- $D$ is the range of $x$.\n",
    "\n",
    "Find $x$ that maximizes the value: $\\begin{aligned}\n",
    "\\max_{x_i \\in D} \\Sigma_i v_i x_i\\\\\n",
    "\\end{aligned}$\n",
    "\n",
    "and constrained by the weight: $\\begin{aligned}\n",
    "\\Sigma_i w_i x_i = C\n",
    "\\end{aligned}$\n",
    "\n",
    "## Problem Versions\n",
    "\n",
    "**Binary Knapsack**\n",
    "\n",
    "Range: $D = \\{0, 1\\}$\n",
    "\n",
    "**Integer Knapsack**\n",
    "\n",
    "Range: $D = [0, b]$\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdbcef63-3c8e-4f0a-a4fb-9058807fa3a8",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Knapsack with binary variables and equality constraint\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0bdc4e6a-199b-44b3-bd3f-fd24722b616b",
   "metadata": {},
   "source": [
    "### Define the optimization problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "83ddbd07-f7ab-4d80-b357-3890622d395f",
   "metadata": {
    "execution": {
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   "source": [
    "import numpy as np\n",
    "import pyomo.environ as pyo\n",
    "\n",
    "\n",
    "def define_knapsack_model(weights, values, max_weight):\n",
    "    model = pyo.ConcreteModel()\n",
    "    num_items = len(weights)\n",
    "\n",
    "    model.x = pyo.Var(range(num_items), domain=pyo.Binary)\n",
    "\n",
    "    x_variables = np.array(list(model.x.values()))\n",
    "\n",
    "    model.weight_constraint = pyo.Constraint(expr=x_variables @ weights == max_weight)\n",
    "\n",
    "    model.value = pyo.Objective(expr=x_variables @ values, sense=pyo.maximize)\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3496c5d6-7df6-49fb-b5b9-5ba48d2b7d62",
   "metadata": {},
   "source": [
    "### Initialize the model with parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "62a98e1c-5dbe-42f9-989e-83ee1df7196b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:29.613987Z",
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     "shell.execute_reply": "2024-05-07T16:07:29.621009Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "knapsack_model = define_knapsack_model(\n",
    "    weights=[2, 3, 2.1, 1, 1, 2], values=[3, 5, 2, 1.5, 1.2, 2.7], max_weight=5\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c78b22a0-9420-44bf-a60d-a7ce136dcaaf",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Setting Up the Classiq Problem Instance\n",
    "\n",
    "In order to solve the Pyomo model defined above, we use the Classiq combinatorial optimization engine. For the quantum part of the QAOA algorithm (`QAOAConfig`) - define the number of repetitions (`num_layers`):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "947293d1-ac9d-41aa-a162-f60ee16608dd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:29.626219Z",
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     "shell.execute_reply": "2024-05-07T16:07:32.194974Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import construct_combinatorial_optimization_model\n",
    "from classiq.applications.combinatorial_optimization import OptimizerConfig, QAOAConfig\n",
    "\n",
    "qaoa_config = QAOAConfig(num_layers=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fdfcb551-77c3-40f4-a54e-4231c7775f19",
   "metadata": {},
   "source": [
    "For the classical optimization part of the QAOA algorithm we define the maximum number of classical iterations (`max_iteration`) and the $\\alpha$-parameter (`alpha_cvar`) for running CVaR-QAOA, an improved variation of the QAOA algorithm [[3](#cvar)]:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "311e4855-403b-4f3d-a64e-7147be629470",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:32.198391Z",
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     "shell.execute_reply": "2024-05-07T16:07:32.200682Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "optimizer_config = OptimizerConfig(max_iteration=60, alpha_cvar=0.7)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f153f82-829a-44df-98c3-47f693d984df",
   "metadata": {},
   "source": [
    "Lastly, we load the model, based on the problem and algorithm parameters, which we can use to solve the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fb32b921-1a7d-4c79-9cb0-6993b0eb6b6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:32.203585Z",
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     "shell.execute_reply": "2024-05-07T16:07:32.321660Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "qmod = construct_combinatorial_optimization_model(\n",
    "    pyo_model=knapsack_model,\n",
    "    qaoa_config=qaoa_config,\n",
    "    optimizer_config=optimizer_config,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78d21bb2-0ac5-471e-a1c9-fb77df0400e7",
   "metadata": {},
   "source": [
    "We also set the quantum backend we want to execute on:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cc69a9e9-38d5-4c91-93dd-ad43bab0ca6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:32.325437Z",
     "iopub.status.busy": "2024-05-07T16:07:32.324838Z",
     "iopub.status.idle": "2024-05-07T16:07:32.335989Z",
     "shell.execute_reply": "2024-05-07T16:07:32.335376Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import set_execution_preferences\n",
    "from classiq.execution import ClassiqBackendPreferences, ExecutionPreferences\n",
    "\n",
    "backend_preferences = ExecutionPreferences(\n",
    "    backend_preferences=ClassiqBackendPreferences(backend_name=\"aer_simulator\")\n",
    ")\n",
    "\n",
    "qmod = set_execution_preferences(qmod, backend_preferences)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a0ee5250-26c9-4329-a69e-d8075d05ebc6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:32.338229Z",
     "iopub.status.busy": "2024-05-07T16:07:32.338050Z",
     "iopub.status.idle": "2024-05-07T16:07:32.351757Z",
     "shell.execute_reply": "2024-05-07T16:07:32.351182Z"
    }
   },
   "outputs": [],
   "source": [
    "from classiq import write_qmod\n",
    "\n",
    "write_qmod(qmod, \"knapsack_binary\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a47c1c9-1b39-4c3a-b291-7e2082fb592e",
   "metadata": {},
   "source": [
    "## Synthesizing the QAOA Circuit and Solving the Problem\n",
    "\n",
    "We can now synthesize and view the QAOA circuit (ansatz) used to solve the optimization problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "10f219ca-2836-4573-9764-70f39b685fca",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:32.354433Z",
     "iopub.status.busy": "2024-05-07T16:07:32.353950Z",
     "iopub.status.idle": "2024-05-07T16:07:36.930198Z",
     "shell.execute_reply": "2024-05-07T16:07:36.929444Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening: https://platform.classiq.io/circuit/db610424-2d79-4db4-8c6c-17734c34b003?version=0.41.0.dev39%2B79c8fd0855\n"
     ]
    }
   ],
   "source": [
    "from classiq import show, synthesize\n",
    "\n",
    "qprog = synthesize(qmod)\n",
    "show(qprog)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce31eb36-c7df-4a0d-9e89-5ef477cc1a8c",
   "metadata": {},
   "source": [
    "We now solve the problem by calling the `execute` function on the quantum program we have generated:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c862d5d4-6d4a-4251-a0e1-d2d55b3a37f3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:36.941434Z",
     "iopub.status.busy": "2024-05-07T16:07:36.941056Z",
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     "shell.execute_reply": "2024-05-07T16:07:43.555252Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from classiq import execute\n",
    "\n",
    "res = execute(qprog).result()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2da3c33-14fe-42c3-ac39-6d4e412ceb74",
   "metadata": {},
   "source": [
    "We can check the convergence of the run:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9172ab29-cfd7-4451-a992-02e50e2ec0ff",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2024-05-07T16:07:43.584830Z"
    },
    "tags": []
   },
   "outputs": [
    {
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",
      "text/plain": [
       "<PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=640x480>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from classiq.execution import VQESolverResult\n",
    "\n",
    "vqe_result = res[0].value\n",
    "vqe_result.convergence_graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d1824ea-80be-4f20-b7ce-d4836483c33f",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Optimization Results"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2736d59a-20db-4d26-9880-affa35b1e4ef",
   "metadata": {},
   "source": [
    "We can also examine the statistics of the algorithm:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b90f0c5-da3a-467e-be89-509ce00ccaea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:43.587793Z",
     "iopub.status.busy": "2024-05-07T16:07:43.587613Z",
     "iopub.status.idle": "2024-05-07T16:07:43.615672Z",
     "shell.execute_reply": "2024-05-07T16:07:43.614990Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>probability</th>\n",
       "      <th>cost</th>\n",
       "      <th>solution</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>0.003</td>\n",
       "      <td>8.0</td>\n",
       "      <td>[1, 1, 0, 0, 0, 0]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.017</td>\n",
       "      <td>7.7</td>\n",
       "      <td>[0, 1, 0, 1, 1, 0]</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>59</th>\n",
       "      <td>0.007</td>\n",
       "      <td>7.7</td>\n",
       "      <td>[0, 1, 0, 0, 0, 1]</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.028</td>\n",
       "      <td>7.5</td>\n",
       "      <td>[1, 1, 0, 1, 0, 0]</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.024</td>\n",
       "      <td>7.2</td>\n",
       "      <td>[0, 1, 0, 1, 0, 1]</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    probability  cost            solution  count\n",
       "62        0.003   8.0  [1, 1, 0, 0, 0, 0]      3\n",
       "24        0.017   7.7  [0, 1, 0, 1, 1, 0]     17\n",
       "59        0.007   7.7  [0, 1, 0, 0, 0, 1]      7\n",
       "2         0.028   7.5  [1, 1, 0, 1, 0, 0]     28\n",
       "11        0.024   7.2  [0, 1, 0, 1, 0, 1]     24"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "from classiq.applications.combinatorial_optimization import (\n",
    "    get_optimization_solution_from_pyo,\n",
    ")\n",
    "\n",
    "solution = get_optimization_solution_from_pyo(\n",
    "    knapsack_model, vqe_result=vqe_result, penalty_energy=qaoa_config.penalty_energy\n",
    ")\n",
    "optimization_result = pd.DataFrame.from_records(solution)\n",
    "optimization_result.sort_values(by=\"cost\", ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c79c0e-6be3-4a27-97dd-3da93dbae5e6",
   "metadata": {},
   "source": [
    "And the histogram:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2709135d-366d-4f27-8eff-4dd28a4545e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:43.618252Z",
     "iopub.status.busy": "2024-05-07T16:07:43.617881Z",
     "iopub.status.idle": "2024-05-07T16:07:43.813012Z",
     "shell.execute_reply": "2024-05-07T16:07:43.812308Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<Axes: title={'center': 'cost'}>]], dtype=object)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "optimization_result.hist(\"cost\", weights=optimization_result[\"probability\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2004e2d2-bcdb-43f8-b068-b8cf5b2beb95",
   "metadata": {},
   "source": [
    "Lastly, we can compare to the classical solution of the problem:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "777c97cb-00c6-46e5-a145-a22f511d66c0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-05-07T16:07:43.815782Z",
     "iopub.status.busy": "2024-05-07T16:07:43.815309Z",
     "iopub.status.idle": "2024-05-07T16:07:43.864718Z",
     "shell.execute_reply": "2024-05-07T16:07:43.863986Z"
    },
    "pycharm": {
     "name": "#%%\n"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model unknown\n",
      "\n",
      "  Variables:\n",
      "    x : Size=6, Index=x_index\n",
      "        Key : Lower : Value                 : Upper : Fixed : Stale : Domain\n",
      "          0 :     0 :    0.9999999999999998 :     1 : False : False : Binary\n",
      "          1 :     0 :                   1.0 :     1 : False : False : Binary\n",
      "          2 :     0 :                   0.0 :     1 : False : False : Binary\n",
      "          3 :     0 :                   0.0 :     1 : False : False : Binary\n",
      "          4 :     0 :                   0.0 :     1 : False : False : Binary\n",
      "          5 :     0 : 2.220446049250313e-16 :     1 : False : False : Binary\n",
      "\n",
      "  Objectives:\n",
      "    value : Size=1, Index=None, Active=True\n",
      "        Key  : Active : Value\n",
      "        None :   True :   8.0\n",
      "\n",
      "  Constraints:\n",
      "    weight_constraint : Size=1\n",
      "        Key  : Lower : Body : Upper\n",
      "        None :   5.0 :  5.0 :   5.0\n"
     ]
    }
   ],
   "source": [
    "from pyomo.opt import SolverFactory\n",
    "\n",
    "solver = SolverFactory(\"couenne\")\n",
    "solver.solve(knapsack_model)\n",
    "\n",
    "knapsack_model.display()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "a07aacdcc8a415e7643a2bc993226848ff70704ebef014f87460de9126b773d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
